• Journal of the Korea Society of Computer and Information
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• v.16 no.3
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• pp.99-107
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• 2011

The feature extraction of asterias amurensis by using patterns is difficult to extract all the concave and convex features of asterias amurensis nor classify concave and convex. Concave and convex as important structural features of asterias amurensis are the features which should be found and the classification of concave and convex is also necessary for the recognition of asterias amurensis later. Accordingly, this study suggests the technique to extract the features of concave and convex, the main features of asterias amurensis. This technique classifies the concave and convex features by using the multi-directional linear scanning and form the candidate groups of the concave and convex feature points and decide the feature points of the candidate groups and apply convex hull algorithm to the extracted feature points. The suggested technique efficiently extracts the concave and convex features, the main features of asterias amurensis by dividing them. Accordingly, it is expected to contribute to the studies on the recognition of asterias amurensis in the future.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.6
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• pp.1635-1656
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• 2023

In Bayesian multi-target tracking, the Poisson multi-Bernoulli mixture (PMBM) filter is a state-of-the-art filter based on the methodology of random finite set which is a conjugate prior composed of Poisson point process (PPP) and multi-Bernoulli mixture (MBM). In order to improve the random finite set-based filter utilized in multi-target tracking of sensor scanning, this paper introduces the Poisson multi-Bernoulli mixture filter into time-matching Bayesian filtering framework and derive a tractable and principled method, namely: the time-matching Poisson multi-Bernoulli mixture (TM-PMBM) filter. We also provide the Gaussian mixture implementation of the TM-PMBM filter for linear-Gaussian dynamic and measurement models. Subsequently, we compare the performance of the TM-PMBM filter with other RFS filters based on time-matching method with different birth models under directional continuous scanning and out-of-order discontinuous scanning. The results of simulation demonstrate that the proposed filter not only can effectively reduce the influence of sampling time diversity, but also improve the estimated accuracy of target state along with cardinality.

• Journal of Korea Technical Association of The Pulp and Paper Industry
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• v.37 no.3
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• pp.23-32
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• 2005

A change of z-directional structural and surface properties by calendering has a great influence on liquid penetration into a sheet. It could be also important for multi-ply sheet because it contacts liquid dunhg coating or converting process. Therefore, this study was aimed to evaluate of a change of z-directional structure in multi-ply sheet by calendering. To do this, multi-ply sheets were prepared with various raw materials and calendered at the different pressure and temperature conditions. In multi-ply sheet which consisted of one kind of pulp fiber, thickness reductions were higher in top and bottom plies than in middle plies. And in the case of soft nip calender treatment with high temperature, top layer which was in contact with heating roll showed the highest reduction of thickness. Hard nip calender treatment showed U-shaped density profile in z-direction, but compression profile by SNC treatment was dependent on calendering condition. To examine z-directional structure of multi-ply sheet which was composed of different raw material for each layer, CLSM (Confocal Laser Scanning Microscopy) analyses were carried out on cross direction of sheet. It turned out to be a useful tool for investigating z-directional analysis. As a result, variation of thickness reduction in z-direction is dependent on ply structure, compressibility of pulp fiber, and calendering condition.

• Proceedings of the Korean Vacuum Society Conference
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• 2014.02a
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• pp.402.1-402.1
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• 2014

Photonic force microscopy (PFM) is an optical tweezers-based scanning probe microscopy, which measures the forces in the range of fN to pN. The low stiffness leads proper to measure single molecular interaction. We introduce a novel photonic force microscopy to stably map various chemical properties as well as topographic information, utilizing weak molecular bond between probe and object's surface. First, we installed stable optical tweezers instrument, where an IR laser with 1064 nm wavelength was used as trapping source to reduce damage to biological sample. To manipulate trapped material, electric driven two-axis mirrors were used for x, y directional probe scanning and a piezo stage for z directional probe scanning. For resolution test, probe scans with vertical direction repeatedly at the same lateral position, where the vertical resolution is ~25 nm. To obtain the topography of surface which is etched glass, trapped bead scans 3-dimensionally and measures the contact position in each cycle. To acquire the chemical mapping, we design the DNA oligonucleotide pairs combining as a zipping structure, where one is attached at the surface of bead and other is arranged on surface. We measured the rupture force of molecular bonding to investigate chemical properties on the surface with various loading rate. We expect this system can realize a high-resolution multi-functional imaging technique able to acquire topographic map of objects and to distinguish difference of chemical properties between these objects simultaneously.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2014.06a
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• pp.79-80
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• 2014

본 논문에서는 다방향 패턴을 이용한 구조광(structured light)방식의 3차원 스캔 기법을 적용하여 3차원 스캐너의 3차원 형상 취득의 정밀도를 높이는 방법을 제안한다. 기존의 단방향 패턴을 이용한 3차원 구조광 방식의 스캔 기법으로 3차원 형상을 취득하는 경우, 스캔 대상 표면의 법선벡터가 패턴의 방향과 수평 할수록 부정확한 형상이 복원된다. 본 논문에서는 스캔 대상의 경사면에 따른 법선 벡터(normal vector) 검출 및 검출된 벡터를 통한 최적의 패턴 방향 선출(quantization), 그리고 각 화소의 최적의 패턴방향을 이용한 선별적인 구조광 방식의 3차원 스캔 기법을 통하여 3차원 형상 취득의 정확성을 높이는 기법을 제안한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.12
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• pp.2693-2700
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• 2009

This paper proposes an effective compression method to utilize the 3D integral image with large amount of data obtained by a lens array in various applications. The conventional compression methods for still images exhibit low performance in terms of coding efficiency and visual quality, since they cannot remove the correlation between elemental images. In the moving picture compression methods, 1D scanning techniques that produce a sequence of elemental images are not enough to remove the directional correlation between elemental images. The proposed method effectively sequences the elemental images from an integral image by the 2D referencing technique and compresses them using the multi-frame referencing of H.264/AVC. The proposed 2D referencing technique selects the optimal reference image according to vertical, horizontal, and diagonal correlation between elemental images. Experimental results show that compression with the sequence of elemental images presents better coding efficiency than that of still image compression. Moreover, the proposed 2D referencing technique is superior to the 1D scanning methods in terms of the objective performance and visual quality.

• Journal of Korea Multimedia Society
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• v.17 no.3
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• pp.277-286
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• 2014

In this paper, we propose a texture feature-based language identification by fusion of Gabor, MDLC (multi-lag directional local correlation), and co-occurrence features. In the proposed method, for a test image, Gabor magnitude images are first obtained by Gabor transform followed by magnitude operator. Moments for the Gabor magniude images are then computed and vectorized. MDLC images are then obtained by MDLC operator and their moments are computed and vectorized. GLCM (gray-level co-occurrence matrix) is next calculated from the test image and co-occurrence features are computed using the GLCM, and the features are also vectorized. The three vectors of the Gabor, MDLC, and co-occurrence features are fused into a feature vector. In classification, the WPCA (whitened principal component analysis) classifier, which is usually adopted in the face identification, searches the training feature vector most similar to the test feature vector. We evaluate the performance of our method by examining averaged identification rates for a test document image DB obtained by scanning of documents with 15 languages. Experimental results show that the proposed method yields excellent language identification with rather low feature dimension for the test DB.